Media Storage Type : 32 GB USB Stick
NPTEL Subject Matter Expert : Prof. A. N. Rajagopalan
NPTEL Co-ordinating Institute : IIT Madras
NPTEL Lecture Count : 90
NPTEL Course Size : 6.1 GB
NPTEL PDF Text Transcription : Available and Included
NPTEL Subtitle Transcription : Available and Included (SRT)
Lecture Titles:
Lecture 1 - Course introduction - 1
Lecture 2 - Course introduction - 2
Lecture 3 - Introduction to Deep Learning - 1
Lecture 4 - Introduction to Deep Learning - 2
Lecture 5 - Introduction to Deep Learning - 3
Lecture 6 - Introduction to Neuron - 1
Lecture 7 - Introduction to Neuron - 2
Lecture 8 - Introduction to Neuron - 3
Lecture 9 - Multilayer Perceptron
Lecture 10 - Regression and classification losses
Lecture 11 - Training a neural network
Lecture 12 - Gradient descent
Lecture 13 - Activation function
Lecture 14 - Backpropagation in MLP - 1
Lecture 15 - Backpropagation in MLP - 2
Lecture 16 - Optimization and Regularization - 1
Lecture 17 - Optimization and Regularization - 2
Lecture 18 - Regularization
Lecture 19 - Dropout
Lecture 20 - Pre-processing
Lecture 21 - Convolutional Neural Networks - 1
Lecture 22 - Convolutional Neural Networks - 2
Lecture 23 - Convolutional Neural Networks - 3
Lecture 24 - CNN Properties
Lecture 25 - Alexnet
Lecture 26 - CNN Architectures - 1
Lecture 27 - CNN Architectures - 2
Lecture 28 - CNN Architectures - 3
Lecture 29 - Introduction to RNN - 1
Lecture 30 - Introduction to RNN - 2
Lecture 31 - Encoder-Decoder models in RNN
Lecture 32 - LSTM
Lecture 33 - Low-level vision - 1
Lecture 34 - Low-level vision - 2
Lecture 35 - Low-level vision - 3
Lecture 36 - Spatial Domain Filtering
Lecture 37 - Frequency Domain Filtering
Lecture 38 - Edge Detection - 1
Lecture 39 - Edge Detection - 2
Lecture 40 - DeepNets for Edge Detection
Lecture 41 - Line detection
Lecture 42 - Feature detectors
Lecture 43 - Harris Corner Detector - 1
Lecture 44 - Harris Corner Detector - 2
Lecture 45 - Harris Corner Detector - 3
Lecture 46 - Blob detection - 1
Lecture 47 - Blob detection - 2
Lecture 48 - Blob detection - 3
Lecture 49 - SIFT - 1
Lecture 50 - SIFT - 2
Lecture 51 - Feature descriptors - 1
Lecture 52 - Feature descriptors - 2
Lecture 53 - SURF - 1
Lecture 54 - SURF - 2
Lecture 55 - Single-View Geometry - 1
Lecture 56 - Single-View Geometry - 2
Lecture 57 - 2D Geometric transformations - 1
Lecture 58 - 2D Geometric transformations - 2
Lecture 59 - Camera intrinsics and extrinsics - 1
Lecture 60 - Camera intrinsics and extrinsics - 2
Lecture 61 - Two-view stereo - 1
Lecture 62 - Two-view stereo - 2
Lecture 63 - Two-view stereo - 3
Lecture 64 - Algebraic representation of epipolar geometry - 1
Lecture 65 - Algebraic representation of epipolar geometry - 2
Lecture 66 - Fundamental matrix computation - 1
Lecture 67 - Fundamental matrix computation - 2
Lecture 68 - Structure from Motion - 1
Lecture 69 - Structure from Motion - 2
Lecture 70 - Structure from Motion - 3
Lecture 71 - Batch processing in SFM
Lecture 72 - Multi-view SFM
Lecture 73 - Factorization methods in SFM
Lecture 74 - Bundle adjustment
Lecture 75 - Dense 3D reconstruction
Lecture 76 - Some results in Stereo and SFM
Lecture 77 - Deepnets for stereo and SFM - 1
Lecture 78 - Deepnets for stereo and SFM - 2
Lecture 79 - Mid-level vision - 1
Lecture 80 - Mid-level vision - 2
Lecture 81 - Lucas-Kanade method for OF
Lecture 82 - Handling large motion in optical flow
Lecture 83 - Image segmentation
Lecture 84 - GMM for clustering
Lecture 85 - Deepnets for Segmentation and OF -1
Lecture 86 - Deepnets for Segmentation and OF -2
Lecture 87 - Deepnets for Segmentation and OF -3
Lecture 88 - Deepnets for Object Detection - 1
Lecture 89 - Deepnets for Object Detection - 2
Lecture 90 - Vision and Language